Work Group Neglected Tropical Diseases, Infection Biology Unit, German Primate Center, Leibniz Institute for Primate Research, Kellnerweg 4, 37077, Göttingen, Germany.
Primate Genetics Laboratory, German Primate Center, Leibniz Institute for Primate Research, Kellnerweg 4, 37077, Göttingen, Germany.
Sci Rep. 2018 Apr 19;8(1):6292. doi: 10.1038/s41598-018-24573-y.
Next-generation sequencing approaches used to characterize microbial communities are subject to technical caveats that can lead to major distortion of acquired data. Determining the optimal sample handling protocol is essential to minimize the bias for different sample types. Using a mock community composed of 22 bacterial strains of even concentration, we studied a combination of handling conditions to determine the optimal conditions for swab material. Examining a combination of effects simulates the reality of handling environmental samples and may thus provide a better foundation for the standardization of protocols. We found that the choice of storage buffer and extraction kit affects the detected bacterial composition, while different 16S rRNA amplification methods only had a minor effect. All bacterial genera present in the mock community were identified with minimal levels of contamination independent of the choice of sample processing. Despite this, the observed bacterial profile for all tested conditions were significantly different from the expected abundance. This highlights the need for proper validation and standardization for each sample type using a mock community and blank control samples, to assess the bias in the protocol and reduce variation across the datasets.
用于描述微生物群落的下一代测序方法受到技术限制的影响，这些限制可能会导致获得的数据发生重大扭曲。确定最佳的样本处理方案对于最大限度地减少不同样本类型的偏差至关重要。我们使用由 22 种浓度均匀的细菌菌株组成的模拟群落来研究组合处理条件，以确定拭子材料的最佳条件。研究组合效应可以模拟处理环境样本的实际情况，从而为标准化方案提供更好的基础。我们发现，储存缓冲液和提取试剂盒的选择会影响检测到的细菌组成，而不同的 16S rRNA 扩增方法只有微小的影响。使用模拟群落和空白对照样品对每种样本类型进行适当的验证和标准化，以评估方案中的偏差并减少数据集之间的差异，从而可以独立于样本处理选择识别模拟群落中存在的所有细菌属，且污染水平最小。尽管如此，所有测试条件下观察到的细菌图谱与预期的丰度有显著差异。这凸显了使用模拟群落和空白对照样品对每种样本类型进行适当验证和标准化的必要性，以评估方案中的偏差并减少数据集之间的差异。
